Potential predictive value of CT radiomics features for treatment response in patients with COVID-19.

Gang HuangZhongyi HuiJialiang RenRuifang LiuYaqiong CuiYing MaYalan HanZehao ZhaoSuzhen LvXing ZhouLijun ChenShisan BaoLianping Zhao

Published in: The clinical respiratory journal (2023)

This new, non-invasive, and low-cost prediction model that combines the radiomics and clinical features is useful for identifying COVID-19 patients who may not respond well to treatment.

Keyphrases

low cost
contrast enhanced
coronavirus disease
lymph node metastasis
sars cov
computed tomography
image quality
dual energy
magnetic resonance
risk assessment
combination therapy
climate change